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<a href="_r_f_classifier_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Random Forest Classifier.</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      K. N. Hansen, O.Krause, J. Kremer</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2011-2012</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_MODELS_TREES_RFCLASSIFIER_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_MODELS_TREES_RFCLASSIFIER_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#include &lt;<a class="code" href="_c_a_r_tree_8h.html">shark/Models/Trees/CARTree.h</a>&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_ensemble_8h.html">shark/Models/Ensemble.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_zero_one_loss_8h.html">shark/ObjectiveFunctions/Loss/ZeroOneLoss.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_squared_loss_8h.html">shark/ObjectiveFunctions/Loss/SquaredLoss.h</a>&gt;</span>        </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_data_view_8h.html">shark/Data/DataView.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {<span class="comment"></span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// \brief Random Forest Classifier.</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">///</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// The Random Forest Classifier predicts a class label</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// using the Random Forest algorithm as described in&lt;br/&gt;</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// Random Forests. Leo Breiman. Machine Learning, 1(45), pages 5-32. Springer, 2001.&lt;br/&gt;</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// It is an ensemble learner that uses multiple decision trees built</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// using the CART methodology. The trees are created using bagging</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// which allows the use the out-of-bag error estimates for an approximately</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// unbiased estimate of the test-error as well as unbiased feature-importance</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// estimates using feature permutation.</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// \ingroup models</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> LabelType&gt;</div>
<div class="foldopen" id="foldopen00061" data-start="{" data-end="};">
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html">   61</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_r_f_classifier.html" title="Random Forest Classifier.">RFClassifier</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_ensemble.html" title="Represents en weighted ensemble of models.">Ensemble</a>&lt;CARTree&lt;LabelType&gt; &gt;{</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    <span class="comment">//OOB-Error for regression</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> VectorType&gt;</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    <span class="keywordtype">double</span> doComputeOOBerror(</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        UIntMatrix <span class="keyword">const</span>&amp; oobPoints, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;VectorType, VectorType&gt;</a> <span class="keyword">const</span>&amp; data</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    ){</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a> = 0;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        <span class="comment">//aquire votes for every element</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> <a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a>(<a class="code hl_function" href="group__shark__globals.html#ga3006553139477e356ee75cd85c190d7c" title="Return the label/output dimensionality of a labeled dataset.">labelDimension</a>(data));</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> input(<a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(data));</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        std::size_t elem = 0;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="keywordflow">for</span>(<span class="keyword">auto</span> <span class="keyword">const</span>&amp; point: data.<a class="code hl_function" href="group__shark__globals.html#ga63e2615845814fee2e8b5773a9d4048f" title="Returns the range of elements.">elements</a>()){</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>            noalias(input) = point.input;</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>            <a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a>.clear();</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>            <span class="keywordtype">double</span> oobWeightSum = 0;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>            <span class="keywordflow">for</span>(std::size_t m = 0; m != this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a1e09f390e1605b270f0ec17b7845e472" title="Returns the number of models.">numberOfModels</a>();++m){</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>                <span class="keywordflow">if</span>(oobPoints(m,elem)){</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>                    oobWeightSum += this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a>(m);</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>                    noalias(<a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a>) += this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a>(m) * this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>(m)(input);</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>                }</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>            }</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>            <a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a> /= oobWeightSum;</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a> += 0.5 * norm_sqr(point.label - <a class="code hl_function" href="namespaceshark.html#a6ae694efca57e84792fcff090223437e" title="Calculates the mean vector of the input vectors.">mean</a>);</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            ++elem;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        }</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a> /= data.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a>;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    }</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    </div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    <span class="comment">//OOB-Error for Classification</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> VectorType&gt;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="keywordtype">double</span> doComputeOOBerror(</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        UIntMatrix <span class="keyword">const</span>&amp; oobPoints, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;VectorType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; data</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    ){</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a> = 0;</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="comment">//aquire votes for every element</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        RealVector votes(<a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(data));</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        RealVector input(<a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(data));</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        std::size_t elem = 0;</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="keywordflow">for</span>(<span class="keyword">auto</span> <span class="keyword">const</span>&amp; point: data.<a class="code hl_function" href="group__shark__globals.html#ga63e2615845814fee2e8b5773a9d4048f" title="Returns the range of elements.">elements</a>()){</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>            noalias(input) = point.input;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>            votes.clear();</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>            <span class="keywordflow">for</span>(std::size_t m = 0; m != this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a1e09f390e1605b270f0ec17b7845e472" title="Returns the number of models.">numberOfModels</a>();++m){</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>                <span class="keywordflow">if</span>(oobPoints(m,elem)){</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>                    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> label = this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>(m)(input);</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>                    votes(label) += this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a>(m);</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>                }</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            }</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a> += (arg_max(votes) != point.label);</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>            ++elem;</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        }</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a> /= data.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a>;</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    }</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    </div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    <span class="comment">//loss for regression</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    <span class="keywordtype">double</span> loss(RealMatrix <span class="keyword">const</span>&amp; labels, RealMatrix <span class="keyword">const</span>&amp; predictions)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        <a class="code hl_class" href="classshark_1_1_squared_loss.html" title="squared loss for regression and classification">SquaredLoss&lt;RealVector, RealVector&gt;</a> loss;</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <span class="keywordflow">return</span> loss.<a class="code hl_function" href="classshark_1_1_squared_loss.html#a008effb959fd46a659caea8c651529f6" title="Evaluate the squared loss .">eval</a>(labels,  predictions);</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    }</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    <span class="comment">//loss for classification</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    <span class="keywordtype">double</span> loss(UIntVector <span class="keyword">const</span>&amp; labels, UIntVector <span class="keyword">const</span>&amp; predictions)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <a class="code hl_class" href="classshark_1_1_zero_one_loss.html" title="0-1-loss for classification.">ZeroOneLoss&lt;unsigned int&gt;</a> loss;</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <span class="keywordflow">return</span> loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(labels,  predictions);</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    }</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    </div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment"></span> </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html#a5104a2e299a21cd91d909fe755db1ea1">  131</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a5104a2e299a21cd91d909fe755db1ea1" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;RFClassifier&quot;</span>; }</div>
</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    </div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    <span class="comment"></span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">    /// \brief Returns the computed out-of-bag-error of the forest</span></div>
<div class="foldopen" id="foldopen00136" data-start="{" data-end="}">
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88">  136</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#acd70da3c9340d47d5d7611e0b35f5d88" title="Returns the computed out-of-bag-error of the forest.">OOBerror</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordflow">return</span> m_OOBerror;</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    }</div>
</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment"></span> </div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span><span class="comment">    /// \brief Returns the computed feature importances of the forest</span></div>
<div class="foldopen" id="foldopen00141" data-start="{" data-end="}">
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html#ad4e4af4f01321b8836554517ef629c0c">  141</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#ad4e4af4f01321b8836554517ef629c0c" title="Returns the computed feature importances of the forest.">featureImportances</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keywordflow">return</span> m_featureImportances;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    }</div>
</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment"></span> </div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment">    /// \brief Counts how often attributes are used</span></div>
<div class="foldopen" id="foldopen00146" data-start="{" data-end="}">
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html#a6cc321d1bcb9c18ba50857d093063730">  146</a></span><span class="comment"></span>    UIntVector <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a6cc321d1bcb9c18ba50857d093063730" title="Counts how often attributes are used.">countAttributes</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        std::size_t n = this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a1e09f390e1605b270f0ec17b7845e472" title="Returns the number of models.">numberOfModels</a>();</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <span class="keywordflow">if</span>(!n) <span class="keywordflow">return</span> UIntVector();</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        UIntVector r = this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>(0).countAttributes();</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        <span class="keywordflow">for</span>(std::size_t i=1; i&lt; n; i++ ) {</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            noalias(r) += this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>(i).countAttributes();</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        }</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordflow">return</span> r;</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    <span class="comment"></span></div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">    /// Compute oob error, given an oob dataset</span></div>
<div class="foldopen" id="foldopen00157" data-start="{" data-end="}">
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html#a336a6c2b13f973dd780644319dab4e8a">  157</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a336a6c2b13f973dd780644319dab4e8a" title="Compute oob error, given an oob dataset.">computeOOBerror</a>(std::vector&lt;std::vector&lt;std::size_t&gt; &gt; <span class="keyword">const</span>&amp; oobIndices, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;RealVector, LabelType&gt;</a> <span class="keyword">const</span>&amp; data){</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        UIntMatrix oobMatrix(oobIndices.size(), data.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>(),0);</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != oobMatrix.size1(); ++i){</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            <span class="keywordflow">for</span>(<span class="keyword">auto</span> index: oobIndices[i])</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>                oobMatrix(i,index) = 1;</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        }</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        m_OOBerror = this-&gt;doComputeOOBerror(oobMatrix,data);</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    }</div>
</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment"></span> </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment">    /// Compute feature importances, given an oob dataset</span></div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="comment">    /// For each tree, extracts the out-of-bag-samples indicated by oobIndices. The feature importance is defined</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span><span class="comment">    /// as the average change of loss (Squared loss or accuracy depending on label type) when the feature is permuted across the oob samples of a tree.</span></div>
<div class="foldopen" id="foldopen00170" data-start="{" data-end="}">
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_classifier.html#a290cb9386878b0264faf15dae5dc2068">  170</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a290cb9386878b0264faf15dae5dc2068">computeFeatureImportances</a>(std::vector&lt;std::vector&lt;std::size_t&gt; &gt; <span class="keyword">const</span>&amp; oobIndices, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;RealVector, LabelType&gt;</a> <span class="keyword">const</span>&amp; data, random::rng_type&amp; rng){</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        std::size_t inputs = <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(data);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        m_featureImportances.resize(inputs);</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <a class="code hl_class" href="classshark_1_1_data_view.html" title="Constant time Element-Lookup for Datasets.">DataView&lt;LabeledData&lt;RealVector, LabelType&gt;</a> <span class="keyword">const</span> &gt; view(data);</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        </div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>        <span class="keywordflow">for</span>(std::size_t m = 0; m != this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a1e09f390e1605b270f0ec17b7845e472" title="Returns the number of models.">numberOfModels</a>();++m){</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            <span class="keyword">auto</span> batch = <a class="code hl_function" href="group__shark__globals.html#ga229ee860771047d3994953fdda9f5a6a" title="Creates a batch given a set of indices.">subBatch</a>(view, oobIndices[m]);</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>            <span class="keywordtype">double</span> errorBefore = this-&gt;loss(batch.label,this-&gt;model(m)(batch.input));</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>            <span class="keywordflow">for</span>(std::size_t i=0; i!=inputs;++i) {</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>                RealVector vOld= column(batch.input,i);</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>                RealVector v = vOld;</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>                std::shuffle(v.begin(), v.end(), rng);</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>                noalias(column(batch.input,i)) = v;</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>                <span class="keywordtype">double</span> errorAfter = this-&gt;loss(batch.label,this-&gt;model(m)(batch.input));</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>                noalias(column(batch.input,i)) = vOld;</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>                m_featureImportances(i) += this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a>(m) * (errorAfter - errorBefore) / batch.size();</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            }</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        }</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        m_featureImportances /= this-&gt;<a class="code hl_function" href="classshark_1_1_ensemble.html#a6965c4363321584b44389670044d24bd" title="Returns the total sum of weights used for averaging.">sumOfWeights</a>();</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    }</div>
</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span> </div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>    <span class="keywordtype">double</span> m_OOBerror; <span class="comment">///&lt; oob error for the forest</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>    RealVector m_featureImportances; <span class="comment">///&lt; feature importances for the forest</span></div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span> </div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>};</div>
</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span> </div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span> </div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>}</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span><span class="preprocessor">#endif</span></div>
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